System and method for embedded cognitive state metric system

ABSTRACT

An embodiment of a method for enabling content personalization for a user based on a cognitive state of the user includes providing an interface configured to enable a third party to request cognitive state data of the user as the user interacts with a content-providing source; establishing bioelectrical contact between a biosignal detector and the user; automatically collecting a dataset from the user; generating a cognitive state metric; receiving a request from the third party for cognitive state data; transmitting the cognitive state data to the third party device; and automatically collecting a dataset from the user as the user engaged tailored content.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/134,822 filed 18 Sep. 2018, which is a continuation of U.S. patentapplication Ser. No. 15/058,622, filed 2 Mar. 2016, which claims thebenefit of U.S. Provisional Application No. 62/127,121 filed 2 Mar.2015, all of which are incorporated in their entirety herein by thisreference.

TECHNICAL FIELD

This invention relates generally to the biosignals field, and morespecifically to a new and useful system and method for enablingpersonalized content based on analysis of biosignals in the biosignalsfield.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of an embodiment of a methodenabling content personalization for a user;

FIG. 2 is a flowchart representation of an embodiment of a methodenabling content personalization for a user;

FIG. 3 is a schematic of an embodiment of a method enabling a thirdparty to personalize content based on cognitive state data of a user

FIG. 4 is a schematic of an embodiment of a method enabling contentpersonalization for a user based on cognitive state data of the user;

FIG. 5 is a schematic of an embodiment of a method enabling contentpersonalization for a user based on data from multiple users;

FIG. 6 is a schematic of an embodiment of a system for enabling contentpersonalization for a user based on cognitive state data of the user.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these preferred embodiments, butrather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIGS. 1 and 2 , an embodiment of a method 100 for enablingcontent personalization for a user based on a cognitive state of theuser comprises: providing an interface configured to enable a thirdparty to request cognitive state data of the user as the user interactswith a content-providing source S110; establishing bioelectrical contactbetween a biosignal detector and the user S115; automatically collectinga dataset from the user S120; generating a cognitive state metric fromthe dataset S130; receiving a request from the third party for cognitivestate data derived from the cognitive state metric S140; transmittingthe cognitive state data to the third party device S145; andautomatically collecting a dataset from the user as the user engagedtailored content generated by the third party in response to thecognitive state data S150.

The method 100 functions to enable a third party to personalize contentdelivered to a user based on cognitive state data of the user engagingwith the third party content. The method is preferably performed with anembodiment, variation, or example of the system described below, but canalternatively be performed with any other suitable system.

In embodiments, the method 100 can include application of a biosignaldetector (e.g., electroencephalogram signal detector) including specifictangible components (e.g., biosignal sensors, motion sensors, processor,communications module, etc.) with specific structure (e.g., specificarrangements of sensors, structure adapted to electrically interface abody region of a user, etc.). Leveraging such structural components, themethod 100 addresses needs in the technological fields of digitalinformation creation based on bioelectrical signals, tagging mediacontent streams with cognitive state data, and real-time digital contentpersonalization through wireless communication between a biosignaldetector, a user device, and a third party device. Further, the method100 confers improvements in the functioning of the user devicesthemselves by providing content tailored to the desires of a user,eliminating power consumption of user devices processing and renderingundesired data.

In particular, the method 100 implements discovered solutions to anissue specifically arising with computer technology, namely the lack ofa streamlined mechanism for enabling a third-party to wirelessly requestand receive cognitive state user data to be used for servingpersonalized digital content to the user. The solutions implemented bythe method 100 include solutions necessarily rooted in computertechnology by: generating objects (e.g., bioelectrical signal data,cognitive state metrics from bioelectrical signal data, etc. etc.)unique to computer technology, and allowing third parties to manipulatethe objects (e.g., request and receive cognitive state data, modify andpresent digital content based on cognitive state data, etc.) in a mannerunique to computer technology (e.g., requesting data through anapplication programming interface, serving content at a wireless userdevice, etc.).

2. Benefits

In specific examples, the method 100 can confer several benefits overconventional methodologies for personalization and annotation of digitaland non-digital content, some of which are provided below. In specificexamples, the method 100 can perform one or more of the following:

First, the method 100 can enable third parties to seamlessly receive andanalyze cognitive state information on users' interactions with thirdparty content (e.g., a website, an application, an advertisement, atelevision show, a movie, a video game, etc.). Facilitating analyticsalong the dimension of cognitive state can give content creators adeeper understanding of how users react and perceive information servedto the user.

Second, content generators can better understand users, and thus, thecontent generators can optimize content for a user or groups of usersbased on their cognitive disposition towards different types of content.For example, for users who have had positive emotion states when viewingcontent relating to movies, content distributors can increase theproportion of movie trailer advertisements served to the user. Thirdparties can gain the ability to iteratively refine content in accordancewith user preferences by presenting content, analyzing user emotions inrelation to the content, updating the content based on the useremotions, re-analyzing user emotions, and repeating. With cognitivestate data, third parties can deliver specific types of content toinduce, avoid, and/or reinforce specific cognitive states of users.

Third, the method 100 can enable third parties or users to efficientlytag content streams (e.g., video streams, social network streams, etc.)with cognitive state data associated with the content stream. Forexample, athletes can record themselves participating in a sportingevent, and the recordings can be tagged with cognitive state dataindicating emotions of the athletes at different time points during thesporting event. Users can thus directly capture and associate theiremotional experiences with activities they partake in.

Fourth, cognitive state data of users can be enhanced with supplementaldata (e.g., motion sensor data, mobile phone data, location, otherusers, etc.) to obtain a fuller understanding of a user's experiencewith, for example, an application or website. For example, a userprofile can be generated for a user based on their cognitive reactionsto different types of media when analyzed in the context of the user'smovements, facial expressions, location, comparisons to other user'sreactions, etc. Content creators can then direct content to the userbased on their user profile.

3. System

As shown in FIG. 6 , an embodiment of a system 300 for providingbioelectrical signal data comprises a biosignal detector 310 including areceiver 320, an analyzer 330, and a communications module 340. In someembodiments, the system 100 can additionally or alternatively include orcommunicate data to and/or from: an underlying data database (e.g.,storing bioelectrical signal data, cognitive state metrics, cognitivestate data, supplemental data), user database (e.g., storing useraccount information, user profiles, devices associated with users, userdemographic information, user interaction history with third partycontent, user preferences, etc.), third party database (e.g., thirdparty account information, content associated with third parties, usersassociated with third parties, third party permissions to accesscognitive state data of users, third party preferences, third partyimplementations of content with a provided application programminginterface, etc.), and/or any other suitable computing system.

Database and/or portions of the method 100 can be entirely or partiallyexecuted, run, hosted, or otherwise performed by: a remote computingsystem (e.g., a server, at least one networked computing system,stateless, stateful), a user device (e.g., a device of a user engagingwith third party content), a third party device (e.g., a device of athird party who requests cognitive state data of users interfacing withthird party content), a fourth party device (e.g., a device of themanufacturer of the biosignal detector, a device of the entity providingthe interface to third parties to retrieve cognitive state data), or byany other suitable computing system.

Devices implementing at least a portion of the method 100 can includeone or more of: a biosignal detector, a smartwatch, smartphone, tablet,desktop, or any other suitable device, as described in more detailbelow. All or portions of the method 100 can be performed by a nativeapplication, web application, firmware on the device, plug-in, or anyother suitable software executing on the device. Device components usedwith the method 100 can include an input (e.g., keyboard, touchscreen,etc.), an output (e.g., a display), a processor, a transceiver, and/orany other suitable component, wherein data from the input device(s)and/or output device(s) can be used can also be collected andtransmitted to entities for analysis (e.g., to generate tailoredcontent). Communication between devices and/or databases can includewireless communication (e.g., WiFi, Bluetooth, radiofrequency, etc.)and/or wired communication.

The system 300 functions to facilitate collection of bioelectricalsignal data (e.g., EEG data) while a user engages in third party contentlinked with an interface (e.g., an application programming interface)enabling third parties to request and/or receive cognitive state data ofthe user. The system 300 preferably enables a variation of the method100 described above, but can alternatively facilitate performance of anysuitable method involving collection and analysis of bioelectricalsignal data for third parties to leverage in tailoring content deliveredto a user.

The biosignal detector 310 functions to collect bioelectrical signaldata from a user. The biosignal detector 310 preferably comprises abioelectrical signal sensor system, wherein the sensor system comprisesa plurality of sensors, each sensor providing at least one channel forbioelectrical signal capture. The plurality of sensors can be placed atspecific locations on the user, in order to capture bioelectrical signaldata from multiple regions of the user. Furthermore, the sensorlocations can be adjustable, such that the biosignal detector 310 istailorable to each user's unique anatomy. Alternatively, the sensorsystem can comprise a single bioelectrical signal sensor configured tocapture signals from a single region of the user. In one example, thebiosignal detector can be a personal EEG device, such as the EmotivInsight neuroheadset, or the Emotiv EPOC neuroheadset, which is shown inFIG. 2 . EEG devices are taught in the U.S. Patent Publication Nos.2007/0066914 (Emotiv) and 2007/0173733 (Emotiv), which are alsoincorporated in their entirety herein by this reference. In variations,the biosignal detector can be that described in U.S. patent applicationSer. No. 13/903,861 filed 28 May 2013, and U.S. patent application Ser.No. 14/447,326 filed 30 Jul. 2014, which are hereby incorporated intheir entirety by this reference.

The receiver 320 functions to receive datasets from a single user ormultiple users. The receiver can include any number or combination of:bioelectrical signal sensors (e.g., EEG), motion sensors (e.g.,accelerometers, gyroscopes), magnetometers, audio sensors, videosensors, location sensors, and/or any suitable type of sensor. Sensorsand/or other components of the receiver 320 can receive any size orcombination of bioelectrical signal data (e.g., EEG data recorded overtime and/or situations, etc.), motion data (e.g., motion along multipleaxes, footsteps, facial movement, etc.), audio data (e.g., audiorecordings from the user, from the user's environment, etc.), video data(e.g., video recordings by the user, of the user, etc.), physical statusdata (e.g., heart rate, galvanic skin response, etc.) and/or anysuitable type of data. Different types of data can be recorded and/orreceived simultaneously, in sequence, and/or in any suitable temporalrelationship. However, the receiver can record and/or receive anysuitable data in determining a user's cognitive state when engaging withcontent. In variations, the receiver and/or types of data can be thatdescribed in U.S. Patent Publication Nos. 2007/0066914 (Emotiv) and2007/0173733 (Emotiv), U.S. patent application Ser. No. 13/903,861 filed28 May 2013, U.S. patent application Ser. No. 14/447,326 filed 30 Jul.2014, and U.S. Provisional No. 62/201,256 field 5 Aug. 2015, which arehereby incorporated in their entirety by this reference.

The analyzer 330 functions to generate an analysis of collectedbioelectrical signal data and any other biosignal, biometric, and/orenvironment data from the user(s), in order to provide the basis forcognitive state data. Additionally or alternatively, the analyzer 330can function to generate user profiles based on the collectedbioelectrical signal data and/or any suitable supplemental dataassociated with a user's interactions with third party content. However,any suitable user device, third party device, and/or fourth party devicecan analyze any suitable data in generating cognitive state data fortransmission to third parties. In variations, the receiver can be thatdescribed in U.S. Patent Publication Nos. 2007/0066914 (Emotiv) and2007/0173733 (Emotiv), U.S. patent application Ser. No. 13/903,861 filed28 May 2013, and U.S. patent application Ser. No. 14/447,326 filed 30Jul. 2014, which are hereby incorporated in their entirety by thisreference.

The communications module 340 functions to enable communication betweenany number or combination of biosignal detectors 310, user devices,third party devices, and/or fourth party device. The communicationsmodule 340 can preferably receive and respond to requests from a userdevice and/or a third party device for cognitive state data of the user.Alternatively, the communications module 340 can transmit cognitivestate data to suitable entities independent of any request for the data.However, any suitable component can receive and/or transmit any suitabledata to any suitable entity. In variations, the communications module340 can be that described in U.S. Patent Publication Nos. 2007/0066914(Emotiv) and 2007/0173733 (Emotiv), U.S. patent application Ser. No.13/903,861 filed 28 May 2013, and U.S. patent application Ser. No.14/447,326 filed 30 Jul. 2014, which are hereby incorporated in theirentirety by this reference.

4. Data Structures

Portions of the system 300 and/or the method 100 can be used with a setof data structures. Data structures can include: cognitive state data,supplemental data, rules, and/or any suitable data structure. In a firstvariation, the types of data structures are predetermined (e.g., by afourth party, by a third party who defines the format of the data to berequested, by a user, etc.). In a second variation, the data structuresare automatically generated. For example, a third-party can define a setof preferences regarding the type of data to be requested regarding auser's interactions with third party content, and the necessary datastructures can be created in response to the third-party set ofpreferences. However, the data structures can be otherwise determined ordefined.

4.1 Data Structures: Cognitive State Data.

The cognitive state data can encompass or be derived from any number orcombination of data types indicative of user cognitive state, including:bioelectrical signal data, cognitive state metrics, heart rate, galvanicskin response, raw data, processed data, and/or any suitable type ofdata providing insight to the cognitive status of a user. Cognitivestate data can be of any number or combination of forms, includingnumerical (e.g., a raw score, normalized score, etc.), verbal (e.g.,high engagement, low interest, medium frustration, etc.), graphical(e.g., facial graphics displaying the relevant cognitive state, facialgraphics representing user facial expressions, colors, etc.), and/or anysuitable form.

Cognitive state data can indicate an emotional state, a cognitiveperformance state, a facial expression, and/or any suitablecharacteristic. Examples of emotional state can include: instantaneousexcitement, long term excitement, stress, engagement, relaxation,interest, focus, frustration, meditation, and/or any suitable emotionalstate. Examples of facial expressions include blink, wink, furrow, browraise, smile, teeth clench, looking in a direction, laughing, smirking,and/or any suitable facial expression

As shown in FIG. 4 , in a first variation, cognitive state data includesnumerical cognitive state metrics measured over time and measureddifferent emotional states of a set of emotional states. For example,based on bioelectrical signal data, cognitive state metrics can begenerated across time points for each emotional state of a set ofemotional states. As shown in FIG. 4 , in a specific example, the set ofemotion states includes interest, engagement, focus, excitement, andrelaxation, and corresponding cognitive state metrics (e.g., at acurrent time point, average over time, etc.) can be 0.8, 0.7, 0.6, 0.4,and 0.3, respectively. In a second variation, cognitive state dataincludes a set of facial expressions that were detected by the biosignaldetector over a given time frame. For example, the cognitive state datacan indicate that a user smiled at a first time point corresponding todisplay of website “A” and that a user clenched their teeth at a secondtime point corresponding to display of website “B”. Facial expressionscan be ascertained from biosignal sensors and/or motion sensorspositioned around a user's facial muscles and/or eyes. Collected datafrom individual sensors can be associated with the specific musclegroups leading to the collected data, and facial expressions can beidentified from such collected data. Additionally or alternatively,facial expressions can be derived from any suitable source of data inany suitable manner. However, cognitive state data can include anysuitable data generated in any suitable fashion based on any suitableconstituents.

4.2 Data Structures: Supplemental Data.

The supplemental data can include any number or combination of datatypes, including: user device data, motion data (e.g., from motionsensors of the biosignal detector), contextual data, raw data, processeddata, and/or any other suitable type of data. User device data caninclude: device component characteristics (e.g., battery life, processorspeed, display, user interface actions allowed, storage, weight, etc.),user interface actions (e.g., a user's swipe, click, keyboard presses,touch, shake, etc.), sensor data (e.g., GPS location, user device motionsensor data, heart rate sensor data, light sensor data, etc.), contentstream data (e.g., recorded media, social network content streams,notification streams, calendar event streams, etc.) and/or any suitableuser device data. Contextual data can include: time of day, weather,environmental surroundings, lighting, location, body posture, and/or anysuitable contextual data.

4.3 Data Structures: Rules.

Rule formats can include: preferences, selections, restrictions,software code, and/or any suitable rule type. Types of rules caninclude: cognitive state data rules, supplemental data rules,communication rules, permission rules, and/or any suitable type ofrules. Cognitive state data rules and/or supplemental data rules caninclude rules relating to time (e.g., when to generate cognitive statedata, when to generate specific types of cognitive state data,generation in response to which portions of the method 100, etc.),mechanism (e.g., which criteria to based cognitive state generationupon, how to analyze raw data in forming processed cognitive state data,generating cognitive state metrics based on comparisons to whichbaselines, to which users, what kind of data to generate, etc.),location (e.g., which component and/or device creates the cognitivestate data, etc.), and/or any suitable type of cognitive state datarule. Communication rules can include rules for when and/or how toreceive, respond, and/or transmit cognitive state data, requests, and/orany suitable type of data. Examples of communication rules includetransmitting data over specific communication links (e.g., wireless,wired, etc.), format of responses to requests, when to process requests,etc. Permission rules can include permission levels for third partyaccounts (e.g., varying access levels to cognitive state data), useraccounts (e.g., varying access levels for users to see a third party'scognitive state analysis leading to how content is delivered to theuser, etc.). However, any suitable type of rule controlling any suitableaspect of the system 300 and/or method 100 can be determined.

Rules can be set by a fourth party (e.g., a biosignal detectormanufacturer, an application programming interface provider, etc.), athird party (e.g., a third party developer, a third party manager of awebsite and/or application, etc.), a user (e.g., a user of the biosignaldetector, a consumer of third party digital content, etc.), and/or anyother suitable entity. Additionally or alternatively, rules can beautomatically determined (e.g., rules generated based definedpreferences of an individual, rules automatically determined in responseto constraints of a user device, etc.). However, any suitable entity canemploy any suitable process for generating rules. Rules are preferablycustomizable with respect to users, third parties, fourth parties,and/or associated accounts, such that different entities can havedifferent permission levels in accordance with the entity's role. Forexample, a user can be given access to set rules regarding how theircognitive state data can be viewed on a user device, while a third partydeveloper can be given access to establish a rule for how frequently torequest cognitive state data from a fourth party database storingcognitive state data of users engaging with the third party's content.

Temporally, rules established by third parties are preferably set duringimplementation of the third party content (e.g., a third partyapplication, website, content source, etc.) with an interface providedto the third party for requesting and receiving user cognitive statedata. Additionally or alternatively, rules set by fourth parties can beset upon the release of a version of the interface. Further, rules setby a user can be set upon a user's configuration of a biosignaldetector. However, rules set by any suitable entity can be establishedat any suitable time. Established rules are preferably updatable by anyentity given the requisite permission level to modify such rules. Ruleupdating can be performed at any suitable time.

In a first variation, communication rules are laid for controlling thereceipt and/or transmission of cognitive state data. A fourth partypreferably controls the potential mechanisms by which a third party canrequest and/or receive data. For example, with respect to the interfaceprovided to third parties, a fourth party can dictate which types ofcontent (e.g., websites, video games, native applications, etc.) canimplement the interface. A third party preferably has a level of controlof when cognitive state data is received. For example, a third party canimplement the interface with a website such that, after every usersession with the website, cognitive state data regarding the usersession is requested and received by the third party. A user canpreferably control the level of involvement of user devices incommunicating cognitive state data. For example, a user can have theoption to facilitate communication of cognitive state data to a thirdparty through a wireless communicable link between a third party deviceand a primary user device rather than a secondary user device. However,any suitable entity can create any suitable communication rule governingthe communication aspects of the system 300 and/or method 100.

In a second variation, cognitive state data rules are employed ininfluencing various aspects of the cognitive state data. A fourth partypreferably has permission to define the process in which cognitive statedata is generated (e.g., based on what criteria, how the criteria isused, etc.). For example, a fourth party can determine that cognitivestate data comprises both cognitive state metrics and supplementalmotion data, where the cognitive state data is generated basedexclusively on data collected at a biosignal detector. A third partypreferably has a level of control over when cognitive state data isgenerated. For example, a third party can define rules that communicateto a biosignal detector to generate cognitive state data in response toparticular user actions (e.g., viewing a specific website, accessingparts of a third party application, clicking on an advertisement, etc.).Users preferably have a degree of control over the types of cognitivestate data collected and/or generated, enabling users to establish rulesin accordance with their personal privacy preferences. However, anysuitable entity can create any suitable cognitive state data rulegoverning the aspects of the cognitive state data.

5. Method

As shown in FIGS. 1 and 2 , an embodiment of a method 100 for enablingcontent personalization for a user based on a cognitive state of theuser comprises: providing an interface configured to enable a thirdparty to request cognitive state data of the user as the user interactswith a content-providing source S110; establishing bioelectrical contactbetween a biosignal detector and the user S115; automatically collectinga dataset from the user S120; generating a cognitive state metric fromthe dataset S130; receiving a request from the third party for cognitivestate data derived from the cognitive state metric S140; transmittingthe cognitive state data to the third party device S145; andautomatically collecting a dataset from the user as the user engagedtailored content generated by the third party in response to thecognitive state data S150. The method 100 can additionally oralternatively include notifying a third party S160, and/or associatingbiosignal data with a content stream S170.

5.1 Providing an Interface.

Block S110 recites: providing an interface configured to enable a thirdparty to request cognitive state data of the user as the user interactswith a content-providing source S110, which functions to enable anentity to request and/or receive cognitive state data associated with auser engaging with content. Examples of interfaces include one or moreof: an application programming interface (API), a web-based interface,an application operable on a third party device, direct access to acognitive state database storing user cognitive state data, and/or anysuitable type of interface. Third parties and/or users can preferablylink aspects of the interface with suitable types of content, in orderto be able to access cognitive state data of the user corresponding tothe user's cognitive state when engaging with the content.

In relation to Block S110, content sourced from the entity(ies) caninclude one or more of: content from applications (e.g., webapplications, apps, native applications, associated advertisementsand/or any other suitable type of application or associated content),television, movies, print media (e.g., books, newspapers, magazines,etc.), video games, and/or any other suitable form of content. Thecontent of Block S110 is preferably third party content, but can beprovided by a fourth party, by a user, and/or any suitable entity. Thirdparty content and/or fourth party content presented to the user can bemodified by the user through, for example, user preferences detectedfrom biosignal data collected at the biosignal detector, manualselection of user preferences, automatic adjustment based on cognitivestate data of the user, and/or any suitable user action. Content can bepresented at a user device, a biosignal detector, a third party device(e.g., a user visiting a third party brick-and-mortar store andinteracting with third party devices presenting content), a fourth partydevice, and/or any suitable instrument. In a first example, theinterface is configured to enable a third party device to requestcognitive state data of the user as the user interacts with a thirdparty application managed by the third party. In a second example, auser seeks to assess the user's own cognitive state data generated whenthe user is engaging in a user's own content. The user can integrate theinterface with the user's content in order to record and receive theuser's cognitive state data.

In relation to Block S110, a computing system of a fourth party (e.g.,Emotiv) preferably provides the interface, which can be implemented by athird party (e.g., web content manager) and/or user. An interface can beprovided before, during, after, and/or in response to creation,verification, or logging into a third party and/or user account.Additionally or alternatively, an interface can be provided in responseto a request by a third party, provided through distributors in responseto requests to distributors by the third party, etc. For example, aninterface can be provided through a software development kitdownloadable by accounts with requisite permission. The interface canadditionally or alternatively be distributed as open source, licensed,through the Internet, through communicable links (e.g., wireless, wired,etc.) with devices, and/or any suitable distribution approach. However,the interface can be provided by any suitable entity in any suitablemanner and at any appropriate time.

In Block S110, the interface is preferably configured to allow a contentproviding source to define the accounts and/or users who will havecognitive state data and/or supplemental data collected and/orgenerated. Users and/or accounts can be selected based on one or moreof: type of content being consumed (e.g., users viewing advertisement“A”, accounts accessing a new feature of an application, etc.),demographic information (education, nationality, ethnicity, age,location, income status, etc.), purchase information (purchase history,frequency, amount, bundle subscription status, etc.), socialcharacteristics (social network usage, social network connections,etc.), device usage characteristics (smartphone usage, applicationusage, etc.), and any other suitable criteria. However, the interfacecan define any suitable preferences with respect to any suitable data.

A first variation of Block S110 can comprise providing an applicationprogramming interface (API) to enable third parties and/or users accessto logging and feedback of user cognitive state data as users interactwith content linked with the API. The API can include a set of routines,protocols, tools, instructions, and/or any suitable information and/orsoftware. The API is preferably configured to allow a third party torequest and/or receive cognitive state data, supplemental data, eventlogs, and/or any suitable data. For example, a wrapper program can beprovided, and individuals can implement the wrapper program with theircontent providing source (e.g., through including simplified commands ofthe wrapper program into website code, app code, etc.).

A second variation of Block S110 comprises providing a web interface.The web interface is preferably accessible over the Internet (e.g.,through a wireless communicable link) by user or third party accountswith requisite permissions. Permitted accounts can link their accountwith any suitable content of the content providing source, and accountscan preferably configure any suitable rule at the web interface withrespect to content and associated consumers of the content. The webinterface can graphically present (e.g., figures, graphs, tables, etc.)cognitive state data, supplemental data, analytics performed on thedata, and/or any suitable information. The presented data can befiltered and/or customized based on user information, user deviceinformation, content type, contextual parameters (e.g., time of day,location, etc.), and/or any suitable criteria.

A third variation of Block S110 comprises providing a downloadable app.The app can be operable on any suitable device (e.g., third partydevice, user device, etc.) by any suitable party and/or account withrelevant permissions. The app can enable rule configuration, dataanalysis, and/or any suitable feature of the interface.

5.2 Establishing Bioelectrical Contact.

Block S115 recites: establishing bioelectrical contact between abiosignal detector and the user, which functions to allow the biosignaldetector to interface with an individual. Establishing bioelectricalcontact is preferably between a biosignal detector and a user, but canalternatively be with a third party and/or any suitable entity.Bioelectrical contact is preferably established through biosignaldetector sensors arranged at a particular location or region of the user(e.g., head region, torso region, etc.). After establishingbioelectrical contact, bioelectrical signal datasets and/or supplementaldatasets (e.g., motion data from motion sensors at the biosignaldetector) can be collected and processed. However, any suitable featureof the biosignal detector can be performed at any suitable time inrelation to establishing bioelectrical contact.

In variations, establishing bioelectrical contact in Block S115 can beperformed in any manner disclosed in U.S. patent application Ser. No.13/903,861 filed 28 May 2013, and U.S. patent application Ser. No.14/447,326 filed 30 Jul. 2014, which are hereby incorporated in theirentirety by this reference.

5.3 Collecting a Dataset from the User.

As shown in FIGS. 1-4 , Block S120 recites: automatically collecting adataset from the user S120, which functions to collect data indicativeof the cognitive state of the user as the user engages content. Thedataset is preferably bioelectrical signal data collected at a biosignaldetector coupled to the user engaging in content. Additionally oralternatively, any suitable dataset (e.g., supplemental data, etc.) canbe collected at the user device, third party device, fourth partydevice, ad/or any suitable device. Any number and/or size of datasetscan be collected.

Temporally, in Block S120, collecting a dataset is preferablyautomatically collected as the user engages content (e.g., a third partyapplication) in an interaction. Datasets can be collected before,during, or after providing the interface to a third party, a third partylinking the interface with third party content, generating a cognitivestate metric, a user interfacing with certain types of content, aparticular user action at the interface of the biosignal detector oruser device, and/or any suitable event. The timing of when to collectdatasets is preferably based upon rules established by a fourth partyand/or third party. Additionally or alternatively, a user can selectpreferences as to the frequency, duration, and timing of datasetcollection. In an example, automatically collecting the firstbioelectrical signal dataset is in response to detecting a login to auser account associated with the user and the third party content, andwhere automatically collecting a second bioelectrical signal dataset isin response to a second login to the user account. However, any suitableentity can control any temporal aspect of collecting datasets.

In relation to Block S120, types of interactions with content caninclude: an introductory interaction (e.g., a users initial interactionwith content that has not previously been presented to the user), arepeat interaction (e.g., an interaction with content that the user haspreviously engaged with), a reference interaction (e.g., a userinteraction with reference content), and/or any suitable type ofinteraction. An interaction can be associated with timeframescorresponding to the entirety or a portion of the user's interactionwith the content or portions of the content. For example, an interactioncan be associated with the duration that the user is on a particularwebpage. However, interactions can be associated with any suitabletimeframe.

In a first variation, Block S120 can include collecting biosignal dataat a biosignal detector as a user interacts with content of a websiteprovided by a third party. In a specific example of this firstvariation, the website is preferably linked by a third party with theinterface provided to the third party, enabling a third party to requestand/or receive biosignal data collected from the user. The userpreferably interacts with the website at a user device wirelesslycoupled with a biosignal detector, but can engage with the website at aninterface of the biosignal detector and/or other suitable device.Biosignal data can be collected from the user as the user engages withspecified pages of the website, for the entirety of the user sessionwith the website, when the user performs a specific user interfaceaction with respect to the website (e.g., clicks on a particular link,zooms in on a specific picture, watches an embedded video, etc.), inresponse to a request transmitted to the biosignal detector from thewebsite or user device, and/or at any suitable timeframe in relation towebsite engagement. However, biosignal data can be collected in anysuitable manner for a user interfacing with the third party website. Inan example of this variation, Block S120 can include collectingbiosignal data for visitors to a website offering a subscriptionservice. Biosignal data can be collected for the visitors as thevisitors go through each step of the sign up process for thesubscription service. Thus, visitors' cognitive state can be directlyaccessed and analyzed as the user goes through each step, in order toenable the website manager to refine steps of the sign up process toproduce specific desired cognitive states of new visitors.

In a second variation, Block S120 can include collecting biosignal dataat a biosignal detector as the user interacts with content of an apprunning on a user device. The software code of the app is preferablyimplemented with the interface provided to the third party, but the appand the interface can be otherwise linked. Data collection can occur asusers engage with specific features of the app linked with theinterface, but can alternatively occur as the user engages any portionof the app. In a specific example, a user is using a video streaming appoperating on the user's smart phone. As different types of videos arewatched by the user, Block S120 can include collecting biosignal datafrom the user to measure a user's cognitive state with respect to thespecific videos being watched. The app manager can defined preferencesfor when or when not to collect data, such as preventing data collectionwhen the user is searching for a video to be subsequently watched.

In a third variation, Block S120 can include collecting biosignal dataas a user interacts with a digital advertisement. In this variation, theadvertisement can be displayed as the user is engaging other types ofcontent (e.g., a commercial break, an advertisement placed at a website,etc.), but can alternatively be independent from other types of content.Biosignal data is preferably collected during any suitable userinteraction with the advertisement, including: clicking on theadvertisement, viewing, zooming in, saving, re-playing, changing thevideo advertisement resolution quality, adjusting the volume of a videoadvertisement, keystrokes, touch, and/or any suitable interaction withthe ad. The user's interactions can be associated (manually,automatically, etc.) with the corresponding portions of the collectedbiosignal data. In a first example of this variation of Block S120, if auser clicked on an advertisement at timeframe “A,” the biosignal datacorresponding to timeframe “A” can be linked with the user click basedon the common time frame. In a second example of this variation of BlockS120, a marketing team associated with a video advertisement could seeka user's first cognitive state at the beginning of the videoadvertisement, and the user's second cognitive state at the climax ofthe video advertisement. The relevant portions of biosignal data can beisolated, where the portions correspond to the time points when the useris viewing the beginning and the climax of the video.

5.3-A Collecting a Dataset from the User—Baseline Dataset

As shown in FIGS. 1 and 2 , Block S120 can be associated withautomatically collecting, at the biosignal detector, a baselinebioelectrical signal dataset from the user S122. In Block S122, abaseline bioelectrical signal dataset is preferably collected as theuser engages reference content in a reference interaction. Referencecontent can be presented to the user in an initial calibration phase ofa user using the biosignal detector. Additionally or alternatively, theuser can go through a validation and registration phase when the userengages third party content, logs into an account, starts using thebiosignal detector, upon request by a third party through the interface,and/or at any suitable time. Collecting a baseline bioelectrical signaldataset in Block S122 can be a threshold requirement before the user canreceive tailored content from a third party. Alternatively, collectingthe baseline dataset can be omitted, but can otherwise be mandatory oroptional with respect to different portions of the method.

The reference content of Block S122 can be configured to induce anysuitable type, number, and/or combination of cognitive states in theuser. Reference content can include any suitable content type. Referencecontent can be defined by a fourth party, a third party, the user,and/or any suitable entity. Reference content can be predetermined(e.g., a set of web pages selected by a third party, various types ofmedia defined by a fourth party, etc.), automatically determined (e.g.,based on user information, contextual information, selected by a machinelearning model generated from user populations, etc.), and/or determinedthrough any suitable manner. For example, reference content can bedefined by a fourth party to include a set of videos and/or picturesselected to induce a wide variety of cognitive states in the user. Whenthe user is setting up the biosignal detector, the user can participatein a calibration phase where baseline bioelectrical signal data iscollected from the user as the user is presented with the referencecontent at a user device wirelessly linked with the biosignal detector.

5.4 Generating a Cognitive State Metric.

As shown in FIGS. 1-4 , Block S130 recites: generating a cognitive statemetric from the dataset S130, which functions to process data in forminga metric indicative of a user's cognitive state. Any number of cognitivestate metrics can be generated at the biosignal detector, a user device,a third party device, a fourth party device, and/or any suitabledevices. In examples where devices other than the biosignal detectorgenerate the cognitive state metric, bioelectrical signal raw datacollected at the biosignal detector can be transmitted to a device, andthe device can subsequently process the raw data and/or other suitabledata in generating the cognitive state metric. The cognitive statemetric of Block S130 preferably indicates a cognitive state of a userwhen the user is in an interaction with content. The cognitive statemetric can be included in cognitive state data (e.g., cognitive statedata transmitted to a third party), but can alternatively exclude thegenerated cognitive state metric.

In Block S130, the cognitive state metric is preferably generated afterbioelectrical signal data is collected during a user's initialinteraction with content. Additionally or alternatively, the cognitivestate metric can be generated after a user's reference interaction withreference content. However, the cognitive state metric can additionallyor alternatively be generated in Block S130 before, during, and/or afterreceiving a third party request for cognitive state data, receivingcognitive state data rules for when and/or how cognitive state metricsare calculated, detecting that a third party has implemented contentwith the interface provided to the third party for accessing cognitivestate data, and/or any other suitable time point.

In a first variation, Block S130 can include generating cognitive statemetrics at specified time intervals. The frequency and/or duration ofthe time intervals can be established by any suitable party associatedwith the method 100, and can be of any suitable frequency and/orduration. In an example of this variation, a fourth party establishes acognitive state data rule that cognitive state metrics are generated forevery minute of recorded bioelectrical signal raw data. In a secondvariation, Block S130 can include generating cognitive state metricswhen a user engages with content in a particular manner. For example,cognitive state metrics can be generated only for timeframes in which auser is interacting (e.g., when user interface actions are detected)with the third party content. In a third variation, Block S130 caninclude generating cognitive state metrics in response to cognitivestate data rules being satisfied. For example, a third party can set arule that limits cognitive state metrics from being generated tosituations where bioelectrical signal data values exceed establishedthresholds. However, cognitive state metrics can be generated in BlockS130 at any suitable time.

In Block S130, the cognitive state metric is preferably generated basedon one or more collected bioelectrical signal datasets. In an example, abaseline cognitive state metric is generated based on the baselinebioelectrical signal dataset collected in Block S122. Generating thecognitive state metric based on the bioelectrical signal dataset caninclude accounting for noise in the dataset, incorporating supplementaldata into the generation of the metric, accounting for rules of how tomap raw data to cognitive state metrics (e.g., rules established by athird party, by a fourth party, etc.), leveraging machine learningmodels, comparing collected biosignal data with previously collecteddata for the user, comparing collected biosignal data with datacollected from other users, and/or any suitable processing step. Forexample, supplemental motion sensor data can be collected at a biosignaldetector as the user engages the content, and the bioelectrical signaldata can be supplemented by the motion sensor data in deriving acognitive state metric. After generating the cognitive state metric, themetric can be associated with types of user interactions with thecontent (e.g., associating the cognitive state metric with a userinterface action at a user device presenting content engaged in by theuser, where the cognitive state data comprises the association betweenthe cognitive state metric and the user interface action. However, thecognitive state metric can be associated with any suitable aspect of theuser, third party, content, and/or other component.

In variations, the cognitive state metric of Block S130 can indicateand/or include any one or more of: emotional state (instantaneousexcitement, long term excitement, stress, engagement, relaxation,interest, focus, frustration, meditation, etc.), a cognitive performancestate (e.g., working memory capacity, long-term memory capacity,execution, mindfulness, etc.), a facial expression (blink, wink, furrow,brow raise, smile, teeth clench, looking in a direction, laughing,smirking, etc.), and/or any suitable characteristic.

In a first variation of this aspect, Block S130 can include calculatingcognitive state metrics based on the history of collected bioelectricalsignal data for a given user. In this variation, bioelectrical signaldata can be collected at different biosignal sensors of the biosignaldetector, and a mapping of the values of the bioelectrical signal datacan be applied to map to association cognitive state metrics. Forexample, high values of EEG signals at sensors proximal the frontal lobecan map to higher cognitive state metrics for specific emotions. Rulesestablishing the mapping between biosignal raw data and cognitive statemetrics can be established by any suitable party. Cognitive statemetrics generated in this variation of Block S130 can be scaled based onhistorical patterns of collected biosignal data for the user. Historicalpatterns can be extracted based on biosignal values, the content thatwas being engaged with the biosignal values were recorded, contextualinformation, and/or any suitable criteria. Self-scaling of cognitivestate metrics can include comparison of collected biosignal data withcollected baseline biosignal data (e.g., collected when the user wasengaging reference content, collected during a validation andregistration phase for the user, etc.).

In a second variation of this aspect, Block S130 can additionally oralternatively include scaling cognitive state metrics with respect toother users. Collected biosignal data for a user can be compared andanalyzed with respect to a group of users defined by any suitable party.In this variation, defining a scaling user group for scaling can bebased on type of content being consumed, demographic information,purchase information, social characteristics, device usagecharacteristics, contextual information, and/or any other suitablecriteria. For example, a scaling user group can be defined based on userage associated with user accounts linked with a third party website, andcognitive state metrics can be generated based on comparison ofbioelectrical signal data of a user relative to other users of similarage. Expected bioelectrical signal data values can be determined forspecific values of specific emotions, and the expected values can varyaccording to scaling user group. Comparing users within a scaling usergroup to calculate a cognitive state metric enables a third party todirectly compare cognitive state metrics collected across differentusers of the scaling user group. For example, a cognitive state metricof “0.7” in “focus” for a first user of a scaling user group can becompared against a cognitive state metric of “0.4” in “focus” for asecond user of the scaling user group, and a third party canappropriately compare the two metrics in inferring that the first userhad a higher focus level when engaging in the corresponding third partycontent. However, determining cognitive state metrics based on multipleusers can otherwise be performed.

In a third variation of this aspect, Block S130 can include applying amachine learning model can to various types of input data in order tooutput an appropriate cognitive state metric indicating the cognitivestate of a user at a given timeframe. A training sample for a trainingsample set can include a set of features and a label indicative of anaspect of the cognitive state metric (e.g., a cognitive state metricvalue, a specific emotion, etc.). Training samples can be manuallylabeled (e.g., users can associate biosignal data corresponding to afirst time frame with types and levels of cognitive states that the usersubjectively felt during the first time frame, etc.), automaticallylabeled, and/or otherwise determined. Features can include:bioelectrical signal data, supplemental data, contextual data, and/orany other suitable feature with a consequential effect on a cognitivestate metric. In a specific example, users of a biosignal detectorundergo a calibration phase, where biosignal data is collected for theuser as the user engages in reference content. The user is asked toindicate (e.g., through a survey) how they felt with respect to theircognitive state during the reference interaction. The data collected canbe used as a feature set and a label for a training sample of a trainingsample set (e.g., generated from multiple user data) upon which amachine learning model can be generated. However, machine learning canotherwise be incorporated in determining a cognitive state metric and/orany suitable data of the method 100.

This variation and/or any other suitable step employing machine learningcan utilize one or more of: supervised learning (e.g., using logisticregression, using back propagation neural networks, using randomforests, decision trees, etc.), unsupervised learning (e.g., using anApriori algorithm, using K-means clustering), semi-supervised learning,reinforcement learning (e.g., using a Q-learning algorithm, usingtemporal difference learning), and any other suitable learning style.Each module of the plurality can implement any one or more of: aregression algorithm (e.g., ordinary least squares, logistic regression,stepwise regression, multivariate adaptive regression splines, locallyestimated scatterplot smoothing, etc.), an instance-based method (e.g.,k-nearest neighbor, learning vector quantization, self-organizing map,etc.), a regularization method (e.g., ridge regression, least absoluteshrinkage and selection operator, elastic net, etc.), a decision treelearning method (e.g., classification and regression tree, iterativedichotomiser 3, C4.5, chi-squared automatic interaction detection,decision stump, random forest, multivariate adaptive regression splines,gradient boosting machines, etc.), a Bayesian method (e.g., naïve Bayes,averaged one-dependence estimators, Bayesian belief network, etc.), akernel method (e.g., a support vector machine, a radial basis function,a linear discriminate analysis, etc.), a clustering method (e.g.,k-means clustering, expectation maximization, etc.), an associated rulelearning algorithm (e.g., an Apriori algorithm, an Eclat algorithm,etc.), an artificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, boostrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm. Eachprocessing portion of the method 100 can additionally or alternativelyleverage: a probabilistic module, heuristic module, deterministicmodule, or any other suitable module leveraging any other suitablecomputation method, machine learning method or combination thereof.

5.5 Receiving a Request

Block S140 recites: receiving a request from the third party forcognitive state data derived from the cognitive state metric S140, whichfunctions to receive a communication from a device requesting cognitivestate data. A request can be received from a third party device, a userdevice, a fourth party device and/or any suitable component. The requestcan be received at a biosignal detector, a fourth party database, aremote server, and/or at any suitable location. In a first variation,Block S140 can include leveraging (e.g., through third party content)the communication ability of the user device accessing the third partycontent to transmit a request to the biosignal detector for cognitivestate data. In a second variation, Block S140 can include, directlyrequesting (e.g., by a third party device) cognitive state informationfrom a biosignal detector, a fourth party database, and/or any suitablecomponent.

Block S140 can include receiving a request through a wirelesscommunicable link between devices (e.g., Bluetooth, WiFi, etc.), a wiredcommunicable link, and/or any suitable connection between devices.Further, receiving a request can require that a specific type ofcommunicable link be present between two devices (e.g., requests willonly be allowed a Bluetooth connection between a biosignal detector anda user device on which content is presented). Received requests can belogged, stored, transmitted to other devices, and/or undergo anysuitable processing step by any suitable component.

In Block S140, requests are preferably received through the interfaceprovided in Block S110, but can be enabled through any suitablecomponent. Requests can be of any form, including: an API request,directly accessing a database, a request at a web interface, a requestfrom an associated app, and/or any suitable form. A third party and/oruser can request any suitable amount or combination of cognitive statedata, supplemental data, contextual data associated with a user engagingin content of the third party and/or user. In an example, a third partydevice requests cognitive state data comprising a baseline cognitivestate metric and a non-baseline cognitive state metric of the user. Thedata retrievable by request can differ based on permission level, thirdparty information, user information, content characteristics and/or anysuitable criteria. However requests can embody any suitablecharacteristic.

In a first variation, Block S140 can include implementing (e.g., withinthird party content) mechanisms enabling a third party to requestcognitive state data. In this variation, requests for cognitive statedata can be initiated and received during a user's interactions with thecontent of the content providing source. For example, requests can beinitiated by software code implemented within the third party content(e.g., with code implemented with website code, app code, etc.), suchthat when a user interacts with the third party content, execution ofthe software code at the user device can include transmission of arequest for cognitive state data. Request and receipt of cognitive statedata during a user session with content allows a third party topersonalize content delivered to the user during the same user session.However, mechanisms for enabling individuals to request data can belinked with content in any suitable fashion.

In a second variation, Block S140 can include initiating (e.g., by athird party) requests for cognitive state data independent of thecontent delivered to the user. Cognitive state data for a userinteraction with content can be recorded, processed, and/or stored inanticipation of future requests by a third party for such cognitivestate data. For example, cognitive state data captured across multipleusers, timeframes, and/or interactions with different types of contentcan be collected and stored before receiving a request by a third partyfor the entirety of the cognitive state data. However, mechanisms forinitiating requests can be implemented in any suitable fashion with anysuitable component.

5.6 Transmitting Cognitive State Data.

As shown in FIGS. 1-3 , Block S145 recites: transmitting the cognitivestate data to the third party device S145, which functions to delivercognitive state data indicating a user's cognitive state as the userengages with content. Cognitive state data is preferably transmitted bya user device, but can additionally or alternatively be transmitted by abiosignal detector, a fourth party device, and/or any suitable device.For example, cognitive state data can be generated by a biosignaldetector in wireless communication with a user device, the biosignaldetector can transmit the cognitive state data to the user device, andthe user device can transmit the cognitive state data to a third partyrequesting the data. However, any suitable entity can transmit and/orreceive cognitive state data through any suitable communication linkbetween components.

Transmitting cognitive state data in Block S145 is preferably performedin response to receiving a request in Block S140, but can additionallyor alternatively be performed before, during, and/or after generation ofcognitive state data, users performing certain user interface actions, athreshold amount of cognitive state data has been collected (e.g., aftera day of cognitive state data has been collected, after specified typesof cognitive state data has been generated, etc.), and/or at anysuitable time.

In Block S145, the transmitted cognitive state data can includecognitive state metrics, supplemental data, associations betweencognitive state data and content (e.g., tags that associate cognitivestate metrics to certain portions of content), raw data (e.g., rawbioelectrical signal data), processed data, analysis (e.g., summaries ofwhat the cognitive state data indicates regarding a user's interactionwith content, comparisons of different users' cognitive states whileviewing the same content), and/or any suitable data. Determination ofthe type, amount, form, and/or other aspect of transmitted cognitivestate data can be predetermined (e.g., by a fourth party, by a thirdparty, etc.), automatically determined (e.g., derived from third partyselected preferences, based on a machine learning model, etc.), and/orotherwise determined.

In a first variation, Block S145 can include communicating cognitivestate data based on temporal criteria. For example, cognitive state datacan be transmitted to a third party immediately after receiving arequest from the third party for the cognitive state data. In anotherexample, cognitive state data is transmitted at regular time intervals(e.g., every hour, every day, every week, etc.), and/or at certain timesof day (e.g., during night time, during non-waking hours, etc.). In asecond variation, transmission can be based on established communicationrules. For example, cognitive state data can be transmitted after athreshold amount of data has been collected for a given number of users.In another example, cognitive state data is communicated only fromand/or to particular devices. In a third variation, cognitive state datais transmitted when the transmitting user device has a sufficient userdevice characteristic. For example, cognitive state data transmissioncan require a particular communication link with a third party device, athreshold state-of-charge, a required amount of free memory, and/or anysuitable user device characteristic.

5.7 Collecting a Dataset from a User Engaging Tailored Content.

As shown in FIGS. 1-4 , Block S150 recites: automatically collecting adataset from the user as the user engaged tailored content generated bythe third party in response to the cognitive state data S150, whichfunctions to collect data indicative of the cognitive state of the useras the user engages content modified for the user. Collecting thedataset S150 is preferably performed after transmitting cognitive statedata to a third party who has analyzed the data and modified contentdelivered to the user in accordance with the analysis. Additionally oralternatively, the dataset can be collected as a user engages contenttailored to the user independent of third party analysis of cognitivestate data. However, the dataset can be collected at before, during,and/or after any suitable portion of the method 100. For example, upondetection that the user is engaging the third party application in asecond interaction, the third party application presenting tailoredcontent to the user based on processing of the baseline cognitive statemetric and the first cognitive state metric: automatically collecting,at the biosignal detector, a second bioelectrical signal dataset can beperformed. Collecting the dataset S150 can be performed within a sameand/or different user session as when untailored content of the contentproviding source was present. For example, datasets can be collectedcorresponding to the first and the second user interactions within asame user session with the content-providing source, where the firstinteraction is with unmodified content, and the second interaction iswith tailored content. As such, collecting a dataset S150 and/or otherportions of the method 100 can be performed in real-time, non-real time,or in any suitable fashion. The dataset collected can correspond to datacollected for the entire duration of the user engaging with the tailoredcontent, and/or for a portion of the user interaction. However,collecting the dataset S150 can be performed at any suitable time.

Similar to Block S120, in Block S150, collected data can include:bioelectrical signal data, supplemental data, contextual data, and/orany suitable form of data as a user engages in an interaction withtailored content. The types of data collected as the user engaged withtailored content can be the same or different from the types of datacollected as the user engaged with unmodified content, referencecontent, and/or any other suitable content. For example, bioelectricalsignal data can be collected for a user's initial interaction with athird party video game. The third party can analyze the bioelectricalsignal data, update the content delivered to the user in the video game,and for subsequent user interactions with the modified content, a morecomprehensive cognitive state dataset (e.g., including bioelectricalsignal data, cognitive metrics, supplemental data, etc.) can be recordedand/or generated to more fully assess the effect of the tailored contenton the user. Between and/or within user interactions with the content,the collected data can differ or remain substantially similar across anysuitable dimension of the data (e.g., size, type of associated content,type of data), user (e.g., different types of data for different users,etc.), and/or other component. However, data collected during a user'sinterfacing with tailored content can include any suitable datacharacteristic.

Collecting the dataset in Block S150 is preferably performed at abiosignal detector (e.g., collecting bioelectrical signal data, motionsensor data, etc.), but can be performed at a user device (e.g.,collecting supplemental data as the user engaged the tailored content),and/or any suitable component.

In a first variation, Block S150 can include leveraging cognitive statedata in selecting suitable types of modified data for inducting,reinforcing, avoiding, and/or otherwise affecting emotional state. In afirst example of the first variation, the cognitive state of the user isa negative emotional state associated with a user's first interactionwith content from a content providing source, and the tailored contentis tailored to induce a positive emotional state of the user (e.g.,through choosing a brighter color palette to present the content,through avoiding content that induced a negative emotional state in theuser, etc.). In a second example of the first variation, the method 100can further include generating an emotion improvement metric based onthe dataset collected for a user engaging in tailored content, where theemotion improvement metric indicates emotional state improvement (e.g.,defined by specific cognitive state metrics, by improvements withspecific desired emotions, etc.) from the first interaction (e.g.,interaction with unmodified content) to the second interaction (e.g.,interaction with tailored content). In a specific example of the firstvariation, a web application includes a set of features, and cognitivestate of a user indicates that the user has more favorable cognitivestates when interacting with a specific feature of the applicationversus other features. In subsequent logins to the user account,tailored information regarding new updates to the specific feature canbe presented to the user (e.g., as opposed to updates concerning otherfeatures of the set of features).

As shown in FIG. 5 , in a second variation, Block S150 can includegenerating user profiles for users. A user profile preferably aggregatesdata of different types captured across multiple instances correspondingto multiple time frames in order to comprehensively indicate a user'scognitive state with respect to different aspects of content. Userprofiles can be generated based on cognitive state data collected fordifferent types of content (e.g., different types of content of a thirdparty, types of content across third parties, etc.), supplemental data,contextual data, comparisons with other user data, and/or any suitabletype of data. For example, additional bioelectrical signal datasets canbe collected for a user as the user engages different third partyapplications. User profiles can be generated by a third party (e.g., athird party device), a fourth party (e.g., a fourth party remote server,etc.), users themselves (e.g., at a user device, at a biosignaldetector, etc.), and/or any suitable entity and/or component. Userprofiles can be transmitted, received, stored, and/or otherwisemanipulated. For example, the method 100 can additionally oralternatively include storing a user profile generated based on abaseline bioelectrical signal data (e.g., collected from a user'sreference interaction), a first bioelectrical signal dataset (e.g.,collected from a user's initial interaction with third party application“A”), a second bioelectrical signal dataset (e.g., collected from auser's repeat interaction with third party application “A” wheretailored content was presented), and a third bioelectrical signaldataset (e.g., collected from a user's interaction with third partyapplication “B”), and subsequent content delivered to the user (e.g., bya third party associated with third party application “A”, “B”, or anyother third party application) can be based on the user profile.

In a third variation, Block S150 can include collecting bioelectricalsignal datasets for multiple users engaging with content tailored basedon analysis of cognitive state data of multiple users. As shown in FIG.5 , for example, content can be tailored for a user group defined basedon similar cognitive state data (e.g., similar cognitive statesassociated with engagement with similar content, similar cognitive statemetric values generally, similar facial expression in reaction to typesof content, etc.), dissimilar cognitive state data, user demographicinformation, contextual information, user profiles, and/or any suitabletype of criteria. Defining user groups can be predetermined (e.g.,manually determined by a third party, fourth party, etc.), automaticallydetermined (e.g., by a machine learning model, by rules established by aparty, etc.), and/or otherwise determined. In one example, collectingbioelectrical signal datasets for multiple users can include:automatically collecting, at a second biosignal detector (e.g. abiosignal detector different from a first biosignal detector associatedwith a first user), a first additional bioelectrical signal dataset froma second user as the second user engages the content; generating anadditional cognitive state metric (e.g., in addition to a cognitivestate metric associated with the first user) based on the firstadditional bioelectrical signal dataset; and in response to receiving asecond request (e.g., a request different from a first request by athird party for cognitive state data of the first user) from the thirdparty device for additional cognitive state data comprising theadditional cognitive state metric, transmitting the additional cognitivestate data to the third party device. In the example, the method 100 canadditionally or alternatively include content tailored for a user groupcomprising the first and the second users, the user group defined by thethird party based on the cognitive state metric and the additionalcognitive state metric. Additionally, the method 100 can additionally oralternatively include automatically collecting, at the biosignaldetector, a second additional bioelectrical signal dataset from thesecond user as the second user engages the tailored content. However,any suitable datasets can be collected for multiple users in anysuitable fashion.

In Block S150, types of tailored content can include: modified websitecontent, advertisements, news, social media, e-mails, broadcast media(e.g., television, radio, etc.), application content, video gaming,etc.). Tailoring of content is preferably performed by a third party,but can be performed by a fourth party, a user, and/or other suitableentity. Content personalization is preferably influenced by a user basedon user preferences, user tagging of content (e.g., a user tagging asocial media post with a positive cognitive state), user thoughts (e.g.,specific user desires inferred from bioelectrical signal data collectedfrom the user by the biosignal detector), and/or other suitable usercriteria. Content tailoring is preferably preformed at the third partydevice administering the content, but can also be performed by othersuitable entities.

Content tailoring in Block S150 is preferably based on analysis ofcognitive state data associated with a user's interaction withunmodified and/or reference content. The analysis associated withproviding tailored content can include comparing cognitive state datawith user baselines (e.g., baseline cognitive state metrics, baselinebioelectrical signal data, etc.), comparing cognitive state metricscaptured at different timeframes, processing with supplemental dataand/or content stream data, employing machine learning models (e.g.,based on values of the cognitive state data, selecting certain forms ofadvertising known to have induced certain cognitive states of the user,etc.), applying thresholds (e.g., if a cognitive state metric exceeds athreshold value for user excitement with the reference content, thendeliver similar content to the reference content, etc.), and/or anysuitable analysis approach. For example, content can be tailored basedon processing a supplemental dataset comprising at least one of sensordata of a user device associated with a third party application, a userinterface action at the user device, and motion sensor data collected atthe biosignal detector as the user engages the third party applicationin the a first interaction (e.g., an interaction with untailoredcontent.). However, any suitable entity can modify content delivered toa user in any suitable fashion.

In a first variation, Block S150 can include tailoring website contentto a user based on cognitive state data. Website text content,advertisements, media assets (e.g., video, images, etc.), features,possible user interfacing options (e.g., allowing users to zoom, click,enter text, etc.), and/or any suitable website content can be customizedfor a user. Modified website aspects can be delivered in real-time(e.g., within a same user session with the website) or in non-real time(e.g., delivered at a repeat interaction between the user and thewebsite). In a specific example, a news website can receive cognitivestate data of a user's cognitive state as the user reads specificarticles published on the news website in an initial interaction betweenthe user and the website. An analysis of the cognitive state data can beperformed (e.g., by an individual associated with the news website, by afourth party, etc.) to determine emotional states of the user's duringthe initial interaction. Based on the analysis, the front page of thewebsite can be tailored to present news articles specific to the user'sinclinations for certain types of articles, where the inclinations areinferred from the analysis. Third parties managing websites can compareuser cognitive state data across multiple users, across multiplewebsites, across different types of website content presented to users,and/or perform any suitable analytics at a fourth party web interfaceprovided to the third party, at a fourth party app, and/or at anysuitable component. However, website content can be modified in anysuitable fashion by any suitable entity.

In a second variation, Block S150 can include tailoring the content ofan app running on a user device. App features, potential userinteraction, affect on the device running the app, and/or any suitableapp content can be tailored for a user. In a specific example, a videogame app can modify the difficulty level of the video game app based onuser's cognitive state data during play of the video game. Beginneraspects (e.g., introductory levels of a video game, basic actions thatcan be performed by a player in the video game, etc.) of the video gamecan be presented to a player, and cognitive state data can be collectedand/or generated as the user engages with the beginner aspects. Thecognitive state data can be analyzed to asses the comfort level of theuser with the beginner aspects, and the subsequent difficulty of thevideo game content delivered to the user can be adjusted based on thecognitive state data (e.g., increasing difficulty level for video gameusers with low engagement cognitive state metrics, decreasing difficultylevel for video game users with cognitive state metrics indicating highfrustration, etc.). However, any suitable app content for any suitabletype of app can be tailored in any suitable fashion based on thecognitive state data.

In a third variation, Blocks S150 can include tailoring advertisementcontent with respect to a user. Advertisement form (e.g., images,videos, text, etc.), product (e.g., tangible goods, intangible goods,media, types of goods, etc.), duration, frequency, interactivity (e.g.,interactive, non-interactive, etc.) and/or any suitable advertisementcontent characteristic can be tailored to a user. In a specific example,advertisements showcasing automobiles can be presented to a user, andbased on cognitive state data associated with the user's viewing and/orinteracting with the advertisement, subsequent advertisements presentedto the user can be tailored based on the analysis of the cognitive statedata. A first user's cognitive state data, when compared to other usersof similar demographic, can show a relative increase in affinity whenviewing automobile advertisements. Further, the cognitive state data canindicate an increased engagement when viewing video-based automobileadvertisements versus image-based automobile advertisements. Based onthis analysis, an increased amount of video-based advertisementsshowcasing automobiles, transportation, mechanical goods, and/or otherconcepts related to automobiles can be presented to the user insubsequent interactions. However, advertisement content can otherwise bemodified in any suitable fashion.

5.8 Notifying.

Block S160 recites: notifying an entity S160, which functions to notifya user and/or a third party of information concerning cognitive statedata. Notifications can include information regarding cognitive statedata (e.g., amount of cognitive state data received, presence ofgenerated cognitive state data, cognitive state data readiness to betransmitted to third parties, type of cognitive state data generated,etc.), supplemental data (e.g., user device data corresponding to auser's engagement with content, etc.), communication data (e.g., receiptof a request, progress of a transmission of cognitive state data to athird party, etc.), events (e.g., presence of a new user from whichcognitive state data can be generated, etc.), and/or any other suitabletype of information. A fourth party entity (e.g., a fourth party remoteserver, a fourth party biosignal detector) preferably generates thenotification, but any suitable entity can generate and/or transmit thenotification. A third party at a third party device is preferablynotified by wireless communication of the notification to the thirdparty device, but a user and/or other suitable entity can be notified.

In relation to Block S160, notifying an entity S160 can includenotifying an entity based on rules (e.g., notification rules set by athird party and/or user influencing the manner and content ofnotification), time (e.g., notification at set frequencies, times ofday, etc.), and/or other criteria. For example, a third party can benotified when thresholds are exceeded for cognitive state metrics, rawdata, and/or other types of data. In a specific example, notifying athird party device can be in response to the difference between thebaseline cognitive state metric and the cognitive state metric exceedinga threshold.

In a first variation, Block S160 can include implementing a pushconfiguration in notifying, where notifications are pushed to thirdparty and/or user devices irrespective of requests by the partyreceiving the notifications. In a first example, a biosignal detectorcan generate cognitive state data indicating a user cognitive statesatisfying certain rules (e.g., a happiness metric exceeding a thresholdwhen browsing a particular website), a notification indicating usersatisfaction can be generated and transmitted to the user device (e.g.,a smartphone used by the user to engage with the particular website),and the user device can transmit the notification to a third partydevice (e.g., a device of the website administrator). In a secondexample, a biosignal detector, upon generation of a notification, candirectly communicate the notification to the third party device. In athird example, a biosignal data device can transmit cognitive state datato a fourth party device (e.g., a fourth party remote server), thefourth party device can generate a notification based on the transmittedcognitive state data, and the fourth party device can communicate thenotification to the third party device. However, any suitable device canparticipate in any suitable communication approach for a pushconfiguration.

In a second variation, Block S160 can include implementing a pullconfiguration in notifying, where notifications can be transmitted tothird parties in response to requests for notifications. Notificationrequests by third parties can be made within or independent from theinterface provided to the third party enabling the third party to accesscognitive state data. In an example, a biosignal detector can recordevents concerning the cognitive state data generated for a user. Throughthe interface provided to the third party, the third party can request alog of such events to become notified of such events. Events and/orother notification information can be handled by a third party contentproviding source (e.g., used in tailoring content to be subsequentlypresented to the user). However, any suitable device can participate inany suitable communication approach for a pull configuration.

5.9 Associating Cognitive State Data with a Content Stream.

Block S170 recites: associating cognitive state data with a contentstream S170, which functions to annotate content streams and/or portionsof content streams with data indicating user cognitive states. Types ofcontent streams can include: media (e.g., self-captured video, othervideo, images, etc.), social network streams (e.g., social networkfriend updates, etc.), news streams, notification streams (e.g., streamsof notifications at a user device, at a biosignal detector, etc.),calendar event streams, and/or any suitable type of content stream.However, any type of cognitive state data, supplemental data, and/orother data can be associated with any suitable content stream possessingany suitable characteristic.

In relation to Block S170, associating cognitive state data with acontent stream can be performed based on tagging (e.g., tagging contentstreams with cognitive state metrics, cognitive states, etc.), uploadingto the content source associated with the content stream (e.g., uploadsto a social network presenting a social network content stream to beassociated with a corresponding cognitive state data), and/or throughany suitable manner. In a variation, associating cognitive state datawith a content stream can additionally or alternatively include:automatically collecting, at the biosignal detector, a supplementalbioelectrical signal dataset over a first timeframe; generating acognitive state metric based on the supplemental bioelectrical signaldata set; associating the cognitive state metric with a content streamcorresponding to the first timeframe, the content stream associated withthe user, wherein the tailored content is based on the cognitive statemetric, the content stream, and the association between the cognitivestate metric and the content stream. However, associating cognitivestate data with a content stream can otherwise be performed.

The content stream of Block S170 preferably corresponds to a time frame(e.g., a time period in which the content stream was captured, a timeframe corresponding to the time period in which bioelectrical signaldata is collected for a user interacting with the content stream, etc.)but can alternatively be independent of a time frame. The time frameassociated with the content stream can be the same, different, and/oroverlapping with the time frame corresponding to bioelectrical signaldataset collected as a user interacts with content. In an example ofBlock S170, a single bioelectrical signal dataset is associated withboth the content stream and a user interaction with third party contentdistinct from the content stream. In a specific example, a contentstream is a user's video recording of themselves playing a video game,where the video game is the third party content that the user isengaging. During this time frame, the user can be coupled to a biosignaldetector that collects a single bioelectrical signal dataset associatedwith the video recording content stream, and with the user'sinteractivity with the video game. Bioelectrical signal datasets can becollected during the entirety or for portions of the content stream timeframe. Further, bioelectrical signal datasets can be associated withcontent streams before, during, and/or after capture or presentation ofthe content streams. However, the content stream and/or correspondingbioelectrical signal dataset can otherwise be associated with anysuitable time frame.

Block S170 can include generating databases that include content streamsassociated (e.g., tagged) with cognitive state data. In Block S170, thedatabases can be defined based on user information, content information,cognitive state data, supplemental data, tag characteristics, and/orother criteria. Such content stream databases can be searched bysuitable parties based on the tagging of the content streams. However,any suitable database can be defined including any suitable contentstream associated with cognitive state data, and such databases can beused for any suitable purpose.

In a first variation, Block S170 can include tagging a video contentstream with cognitive state data. Specific time points, time periods,and/or the entire content stream can be tagged with any portion or theentirety of the cognitive state data. Tagging can be manual (e.g., aparty can select portions of the video to tag with cognitive statescorresponding to the time frames of the portions, etc.), automatic(e.g., a fourth party remote server can automatically tag portions ofthe content stream based on mapping time stamps of the cognitive statedata to time stamps of the content stream), and/or otherwise performed.In a first specific example, a user who has used cognitive state data totag their performances in various sporting events can search thedatabase for the instances in which the cognitive state metricsindicated a high focus from the user. In a second specific example, askier video-records him or herself skiing down snow slopes whilecognitive state data is generated for the skier. The video recordingcontent stream can be annotated with the cognitive state data,indicating when a user experienced the most excitement (e.g., when theskier is making sharp turns). Such analysis can be leveraged by athird-party skiing equipment vendor who is managing a e-commerce storefrequently visited by the skier. Skiing equipment tailored for the user(e.g., for making sharp turns) can be presented to the skier in lieu ofother types of equipment. However, cognitive state data can otherwise beused for tagging video content streams, and the tags can be used for anyother suitable purpose.

In a second variation, Block S170 can include associating cognitivestate with a social network content stream. Cognitive state data and/orcognitive states indicated by such data can be associated with posts(e.g., friend posts, media posts, news posts, etc.) in the contentstream, user actions affecting the content stream (e.g., a userassociating their cognitive state with a user video upload to the socialnetwork website). Cognitive state data associated with social networkcontent streams can be transmitted to parties associated with the socialnetwork and/or other content source providers. Parties can subsequentlyanalyze the cognitive state data in the context of the social networkcontent stream, tailor content based on the analysis, and bioelectricalsignal data can subsequently be automatically collected as users engagethe tailored content. In a specific example, analysis of a user'scognitive state data associated with the user's social network contentstream can indicate that a user's cognitive state has been positive whenviewing friends' photos of nature and nature activities. The socialnetwork can leverage this cognitive state data to include morenature-based content in the content stream presented to the user. Othercontent source providers can utilize this data in selecting nature-basedcontent (e.g., website content, advertisements, etc.) tailored to theuser. However, cognitive state data can otherwise be associated with asocial network content stream, and can be used for any other suitablepurpose.

The method 100 and system 300 of the preferred embodiment and variationsthereof can be embodied and/or implemented at least in part as a machineconfigured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are preferably executedby computer-executable components preferably integrated with the system300 and one or more portions of a processor, a controller, and/or othersystem components. The computer-readable medium can be stored on anysuitable computer-readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component is preferably ageneral or application specific processor, but any suitable dedicatedhardware or hardware/firmware combination device can alternatively oradditionally execute the instructions.

The FIGURES illustrate the architecture, functionality and operation ofpossible implementations of systems, methods and computer programproducts according to preferred embodiments, example configurations, andvariations thereof. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block can occurout of the order noted in the FIGURES. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

As a person skilled in the field of biosignals will recognize from theprevious detailed description and from the figures and claims,modifications and changes can be made to the preferred embodiments ofthe invention without departing from the scope of this invention definedin the following claims.

We claim:
 1. A method comprising: for each user of a set of users:establishing bioelectrical contact between a head-mounted bioelectricalsensor and the user at a plurality of head regions of the user;automatically collecting a bioelectrical dataset from the user as theuser engages a first stimulus from a content provider, wherein thebioelectrical dataset comprises bioelectrical data measured from theplurality of head regions of the user; determining a cognitive statemetric of the user based on the bioelectrical dataset using a firstmodel; automatically collecting a second bioelectrical dataset from theuser as the user engages a second stimulus; and determining a secondcognitive state metric based on the second bioelectrical dataset usingthe first model; generating a set of user groups based on the firststimulus, the cognitive state metric, the second stimulus, and thesecond cognitive state metric; using a second model, selecting a usergroup from the set of user groups based on a cognitive state metric ofan individual, wherein the individual is not within the set of users,wherein the second model comprises a machine learning model; determiningpersonalized content for the individual based on the user group; andpresenting the personalized content to the individual.
 2. The method ofclaim 1, wherein the first stimulus comprises a product.
 3. The methodof claim 1, wherein the head-mounted bioelectrical sensor does notobstruct a user's sense of smell.
 4. The method of claim 1, wherein thecognitive state metric is indicative of a cognitive state associatedwith brain activity from a plurality of brain lobes of the user, whereinthe plurality of brain lobes are associated with the plurality of headregions of the user.
 5. The method of claim 4, wherein at least twobrain lobes of the plurality of brain lobes are associated with a commonbrain hemisphere of the user.
 6. The method of claim 1, wherein thecognitive state metric is indicative of a cognitive state of the usercomprising at least one of excitement, stress, engagement, relaxation,interest, or focus of the user.
 7. The method of claim 1, furthercomprising, for each user of the set of users: contemporaneouslycollecting the bioelectrical dataset and storing a stimulus streamassociated with at least one of the first stimulus or the secondstimulus; and associating the stimulus stream with the cognitive statemetric.
 8. The method of claim 1, further comprising administeringsurveys comprising purchase information to each user of the set ofusers, wherein the set of user groups is further determined based on theset of users' responses to the surveys.
 9. The method of claim 1,further comprising, for each user of the set of users, collecting amotion dataset using a motion sensor detector connected to thehead-mounted bioelectrical sensor as the user engages the first stimulusand the second stimulus; wherein the cognitive state metric is furtherdetermined based on the motion dataset and wherein the set of usergroups are further generated based on the motion dataset.
 10. The methodof claim 1, wherein the personalized content is personalized to induce apositive cognitive state of the individual.
 11. The method of claim 1,wherein the cognitive state metric is determined using a machinelearning method.
 12. A method comprising: for each training user in aset of training users: measuring bioelectrical signals; and using afirst model, determining cognitive state data based on the bioelectricalsignals; determining user groups from the set of training users, whereindetermining user groups comprises classifying a cognitive state of eachtraining user of the set of training users, wherein the classificationsof each cognitive state are used to group the training users into usergroups; training a second model to output a user group associated with atraining user based on the cognitive state data associated with thetraining user, wherein the second model comprises a machine learningmodel; establishing bioelectrical contact between a biosignal detectorand a user, wherein the biosignal detector is coupled to a plurality ofhead regions of the user corresponding to a plurality of brain lobes ofthe user; automatically collecting, at the biosignal detector, abioelectrical signal dataset from the user as the user interacts withstimuli provided by a third party; using the first model, determiningcognitive state data associated with the user, indicative of a cognitivestate of the user as the user interacts with the stimuli, from thebioelectrical signal dataset, wherein the cognitive state data isassociated with brain activity from the plurality of brain lobes of theuser wherein the cognitive state data is determined using a machinelearning method trained using a training dataset generated by measuringbioelectrical signals and cognitive state data associated with atraining group of users interacting with training stimuli from the thirdparty; using the second model, selecting a user group based on thecognitive state data associated with the user; presenting tailoredstimuli, generated based on an analysis of the selected user group,wherein the tailored stimuli is further determined based on the stimuliand the association between the cognitive state data for the user andthe stimuli.
 13. The method of claim 12, wherein a stimulus of thestimuli comprises a visual stimulus.
 14. The method of claim 12, whereinthe biosignal detector does not obstruct a user's sense of smell. 15.The method of claim 12, wherein the cognitive state comprises at leastone of excitement, stress, engagement, relaxation, interest, or focus ofthe user.
 16. The method of claim 12, further comprising administeringsurveys comprising purchase information to each training user of the setof training users, wherein the user groups are generated based onresponses to the surveys associated with each training user of the setof training users.
 17. The method of claim 12, wherein the tailoredstimuli are further generated based on an analysis of a motion sensordataset collected at the biosignal detector as the user interacts withthe stimuli.
 18. The method of claim 12, wherein the tailored stimulicomprise a portion of the stimuli.
 19. The method of claim 12, whereintraining the second model comprises training the second model using aregularization method.